
import argparse
import json
import os
import re
import shutil

import torch

from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging


logging.set_verbosity_warning()

__lowerCAmelCase     :	Any       			=  2




class 			UpperCAmelCase_     :


  '''simple docstring'''







  def __init__(					self     :					List[Any]     ,				*,  # begin keyword-only arguments
      UpperCamelCase__     :					Any="<s>"     ,				UpperCamelCase__     :					str="<pad>"     ,				UpperCamelCase__     :					List[Any]="</s>"     ,				UpperCamelCase__     :					Optional[int]="<unk>"     ,				UpperCamelCase__     :					List[str]=None     ,				)     ->      Dict:







    """simple docstring"""






    __magic_name__	,       __magic_name__	,       __magic_name__	,       __magic_name__								=			bos, unk, pad, eos
    __magic_name__								=			[]
    __magic_name__								=			[]
    __magic_name__								=			{}
    __magic_name__								=			self.add_symbol(UpperCamelCase__       )
    __magic_name__								=			self.add_symbol(UpperCamelCase__       )
    __magic_name__								=			self.add_symbol(UpperCamelCase__       )
    __magic_name__								=			self.add_symbol(UpperCamelCase__       )
    if extra_special_symbols:
      for s in extra_special_symbols:
        self.add_symbol(UpperCamelCase__       )
    __magic_name__								=			len(self.symbols       )
  def __eq__(					self     :					Any     ,				UpperCamelCase__     :					Dict       )     ->      Optional[int]:







    """simple docstring"""






    return self.indices == other.indices
  def __getitem__(					self     :					Union[str, Any]     ,				UpperCamelCase__     :					Union[str, Any]       )     ->      List[Any]:







    """simple docstring"""






    if idx < len(self.symbols       ):
      return self.symbols[idx]
    return self.unk_word
  def __len__(					self     :					Any       )     ->      Optional[int]:







    """simple docstring"""






    return len(self.symbols       )
  def __contains__(					self     :					int     ,				UpperCamelCase__     :					List[Any]       )     ->      List[str]:







    """simple docstring"""






    return sym in self.indices
  @classmethod
  def 				_lowercase				(					cls     :					int     ,				UpperCamelCase__     :					Optional[Any]       )     ->      Optional[Any]:







    """simple docstring"""






    __magic_name__								=			cls()
    d.add_from_file(UpperCamelCase__       )
    return d
  def 				_lowercase				(					self     :					List[str]     ,				UpperCamelCase__     :					List[str]     ,				UpperCamelCase__     :					Optional[Any]=1     ,				UpperCamelCase__     :					Optional[Any]=False       )     ->      Tuple:







    """simple docstring"""






    if word in self.indices and not overwrite:
      __magic_name__								=			self.indices[word]
      __magic_name__								=			self.count[idx] + n
      return idx
    else:
      __magic_name__								=			len(self.symbols       )
      __magic_name__								=			idx
      self.symbols.append(UpperCamelCase__       )
      self.count.append(UpperCamelCase__       )
      return idx
  def 				_lowercase				(					self     :					int     ,				UpperCamelCase__     :					Dict       )     ->      int:







    """simple docstring"""






    return 0





  def 				_lowercase				(					self     :					List[str]     ,				UpperCamelCase__     :					Any       )     ->      Union[str, Any]:







    """simple docstring"""






    if isinstance(UpperCamelCase__     ,				UpperCamelCase__       ):
      try:
        with open(UpperCamelCase__     ,				"""r"""     ,				encoding="""utf-8"""       ) as fd:
          self.add_from_file(UpperCamelCase__       )
      except FileNotFoundError as fnfe:
        raise fnfe
      except UnicodeError:
        raise Exception("""Incorrect encoding detected in {}, please rebuild the dataset""".format(UpperCamelCase__       )       )
      return

    __magic_name__								=			f.readlines()
    __magic_name__								=			self._load_meta(UpperCamelCase__       )

    for line in lines[indices_start_line:]:
      try:
        __magic_name__	,       __magic_name__								=			line.rstrip().rsplit(""" """     ,				1       )
        if field == "#fairseq:overwrite":
          __magic_name__								=			True
          __magic_name__	,       __magic_name__								=			line.rsplit(""" """     ,				1       )
        else:
          __magic_name__								=			False
        __magic_name__								=			int(UpperCamelCase__       )
        __magic_name__								=			line
        if word in self and not overwrite:
          raise RuntimeError(
              """Duplicate word found when loading Dictionary: '{}'. """
              """Duplicate words can overwrite earlier ones by adding the """
              """#fairseq:overwrite flag at the end of the corresponding row """
              """in the dictionary file. If using the Camembert model, please """
              """download an updated copy of the model file.""".format(UpperCamelCase__       )       )
        self.add_symbol(UpperCamelCase__     ,				n=UpperCamelCase__     ,				overwrite=UpperCamelCase__       )
      except ValueError:
        raise ValueError("""Incorrect dictionary format, expected '<token> <cnt> [flags]'"""       )







def a__       (			A_   ):






  '''simple docstring'''





  __magic_name__								=			dict((re.sub(R"""@@$""",	"""""",	A_   ), v) if k.endswith("""@@"""   ) else (re.sub(R"""$""",	"""</w>""",	A_   ), v) for k, v in d.items()   )
  __magic_name__								=			"""<s> <pad> </s> <unk>""".split()
  # restore the special tokens
  for k in keep_keys:
    del da[f'''{k}</w>''']
    __magic_name__								=			d[k]  # restore
  return da

def a__       (			A_,	A_   ):






  '''simple docstring'''





  if not os.path.exists(A_   ):
    raise ValueError(f'''path {biogpt_checkpoint_path} does not exist!'''   )
  os.makedirs(A_,	exist_ok=A_   )
  print(f'''Writing results to {pytorch_dump_folder_path}'''   )

  # handle various types of models

  __magic_name__								=			os.path.join(A_,	"""checkpoint.pt"""   )
  if not os.path.isfile(A_   ):
    raise ValueError(f'''path to the file {checkpoint_file} does not exist!'''   )
  __magic_name__								=			torch.load(A_,	map_location="""cpu"""   )

  __magic_name__								=			chkpt["""cfg"""]["""model"""]

  # dicts
  __magic_name__								=			os.path.join(A_,	"""dict.txt"""   )
  if not os.path.isfile(A_   ):
    raise ValueError(f'''path to the file {dict_file} does not exist!'''   )
  __magic_name__								=			Dictionary.load(A_   )
  __magic_name__								=			rewrite_dict_keys(src_dict.indices   )
  __magic_name__								=			len(A_   )
  __magic_name__								=			os.path.join(A_,	VOCAB_FILES_NAMES["""vocab_file"""]   )
  print(f'''Generating {src_vocab_file} of {src_vocab_size} records'''   )
  with open(A_,	"""w""",	encoding="""utf-8"""   ) as f:
    f.write(json.dumps(A_,	ensure_ascii=A_,	indent=A_   )   )

  # merges_file (bpecodes)
  __magic_name__								=			os.path.join(A_,	"""bpecodes"""   )
  if not os.path.isfile(A_   ):
    raise ValueError(f'''path to the file {bpecodes_file} does not exist!'''   )

  __magic_name__								=			os.path.join(A_,	VOCAB_FILES_NAMES["""merges_file"""]   )
  shutil.copyfile(A_,	A_   )

  # model config
  __magic_name__								=			os.path.join(A_,	"""config.json"""   )

  __magic_name__								=			{
      """activation_dropout""": args["""activation_dropout"""],
      """architectures""": ["""BioGptForCausalLM"""],
      """attention_probs_dropout_prob""": args["""attention_dropout"""],
      """bos_token_id""": 0,
      """eos_token_id""": 2,
      """hidden_act""": args["""activation_fn"""],
      """hidden_dropout_prob""": args["""dropout"""],
      """hidden_size""": args["""decoder_embed_dim"""],
      """initializer_range""": 0.02,
      """intermediate_size""": args["""decoder_ffn_embed_dim"""],
      """layer_norm_eps""": 1e-12,
      """layerdrop""": args["""decoder_layerdrop"""],
      """max_position_embeddings""": args["""max_target_positions"""],
      """model_type""": """biogpt""",
      """num_attention_heads""": args["""decoder_attention_heads"""],
      """num_hidden_layers""": args["""decoder_layers"""],
      """pad_token_id""": 1,
      """scale_embedding""": not args["""no_scale_embedding"""],
      """tie_word_embeddings""": args["""share_decoder_input_output_embed"""],
      """vocab_size""": src_vocab_size,
  }

  # good hparam defaults to start with

  print(f'''Generating {biogpt_model_config_file}'''   )
  with open(A_,	"""w""",	encoding="""utf-8"""   ) as f:
    f.write(json.dumps(A_,	ensure_ascii=A_,	indent=A_   )   )

  # tokenizer config
  __magic_name__								=			os.path.join(A_,	A_   )

  __magic_name__								=			{
      """bos_token""": """<s>""",
      """eos_token""": """</s>""",
      """model_max_length""": 1024,
      """pad_token""": """<pad>""",
      """special_tokens_map_file""": None,
      """tokenizer_class""": """BioGptTokenizer""",
      """unk_token""": """<unk>""",
  }

  print(f'''Generating {biogpt_tokenizer_config_file}'''   )
  with open(A_,	"""w""",	encoding="""utf-8"""   ) as f:
    f.write(json.dumps(A_,	ensure_ascii=A_,	indent=A_   )   )

  # model
  __magic_name__								=			chkpt["""model"""]

  # remove unneeded keys
  __magic_name__								=			[
      """decoder.version""",
  ]
  for k in ignore_keys:
    model_state_dict.pop(A_,	A_   )

  __magic_name__								=			list(model_state_dict.keys()   )
  for layer_name in layer_names:
    if layer_name.endswith("""output_projection.weight"""   ):
      __magic_name__								=			model_state_dict.pop(A_   )
    else:
      __magic_name__								=			model_state_dict.pop(A_   )

  __magic_name__								=			BioGptConfig.from_pretrained(A_   )
  __magic_name__								=			BioGptForCausalLM(A_   )

  # check that it loads ok
  model_new.load_state_dict(A_   )

  # save
  __magic_name__								=			os.path.join(A_,	A_   )
  print(f'''Generating {pytorch_weights_dump_path}'''   )
  torch.save(A_,	A_   )

  print("""Conversion is done!"""   )


if __name__ == "__main__":
    __lowerCAmelCase     :	Dict       			=  argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '--biogpt_checkpoint_path',
        default=None,
        type=str,
        required=True,
        help=(
            'Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,'
            ' bpecodes, etc.'
        ),
    )
    parser.add_argument(
        '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
    )
    __lowerCAmelCase     :	Any       			=  parser.parse_args()
    convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)



import math

def a__       (			A_   ):






  '''simple docstring'''





  if 1 < number < 4:
    # 2 and 3 are primes
    return True
  elif number < 2 or number % 2 == 0 or number % 3 == 0:
    # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
    return False

  # All primes number are in format of 6k +/- 1
  for i in range(5,	int(math.sqrt(A_   ) + 1   ),	6   ):
    if number % i == 0 or number % (i + 2) == 0:
      return False
  return True

def a__       (			A_ = 0.1   ):






  '''simple docstring'''





  __magic_name__								=			3
  __magic_name__								=			3

  while primes / (2 * j - 1) >= ratio:
    for i in range(j * j + j + 1,	(j + 2) * (j + 2),	j + 1   ):
      primes += is_prime(A_   )
    j += 2
  return j


if __name__ == "__main__":
    import doctest

    doctest.testmod()

